地球资源数据云——数据资源详情

Twitter 数据集的七个 NLP 任务

该数据集《Seven NLP Tasks With Twitter Datasets》主要用于多分类任务，数据形态以文本为主，应用场景偏向文本内容分析。 题目说明：Multi - class tweet classification 任务类型：文本多分类。 建议流程：先做文本清洗与分词，再比较 TF - IDF+线性模型 与 预训练语言模型。 评估建议：使用分层切分或交叉验证，优先关注 F1、Recall、AUC 等分类指标。 可用文件：未检测到标准 CSV，可优先查看目录中的索引或说明文件。 Context The experimental landscape in natural language processing for social media is too fragmented. Each year, new shared tasks and datasets are proposed, ranging from classics like sentiment analysis to irony detection or emoji prediction. Therefore, it is unclear what the current state of the art is, as there is no standardized evaluation protocol, neither a strong set of baselines trained on such domainspecific data. The propose of this dataset is presenting evaluation consisting of seven heterogeneous Twitter - specific classification tasks. Content This dattaset consists of seven heterogenous tasks in Twitter, all framed as multi - class tweet classification. Each dataset presented in the same format and with fixed training, validation and test splits.